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Super-resolution benefit for face recognition

机译：人脸识别的超分辨率优势

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摘要

Vast amounts of video footage are being continuously acquired by surveillance systems on private premises, commercial properties, government compounds, and military installations. Facial recognition systems have the potential to identify suspicious individuals on law enforcement watchlists, but accuracy is severely hampered by the low resolution of typical surveillance footage and the far distance of suspects from the cameras. To improve accuracy, super-resolution can enhance suspect details by utilizing a sequence of low resolution frames from the surveillance footage to reconstruct a higher resolution image for input into the facial recognition system. This work measures the improvement of face recognition with super-resolution in a realistic surveillance scenario. Low resolution and super-resolved query sets are generated using a video database at different eye-to-eye distances corresponding to different distances of subjects from the camera. Performance of a face recognition algorithm using the super-resolved and baseline query sets was calculated by matching against galleries consisting of frontal mug shots. The results show that super-resolution improves performance significantly at the examined mid and close ranges.
机译：监视录像系统不断被私人场所，商业物业，政府大楼和军事设施上的监控系统所采集。面部识别系统具有识别执法观察名单上可疑人员的潜力，但由于典型监视录像的分辨率低以及嫌疑人离摄像机的距离太远，准确性严重受到阻碍。为了提高准确性，超分辨率可以通过利用来自监视镜头的一系列低分辨率帧来重建更高分辨率的图像以输入到面部识别系统中，从而增强可疑细节。这项工作可以在现实的监视场景中以超分辨率来衡量人脸识别的改进。低分辨率和超分辨查询集是使用视频数据库生成的，该视频数据库在与主体到相机的不同距离相对应的不同眼睛之间的距离处。通过与由正面马克杯镜头组成的图库相匹配，计算出使用超分辨和基线查询集的面部识别算法的性能。结果表明，在检查的中距离和近距离处，超分辨率可显着提高性能。

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